Penetration Testing for Websites using LLM

Despite growing interest in applying Large Lan-guage Models (LLMs) to security assessment, existing prototype systems rarely provide controlled benchmarks, reproducible ar-tifacts, significance testing, or structured human evaluation. We introduce a reproducible, safety-bounded LLM-augmented web assessment framework that layers an LLM reasoning module atop conventional scanners to perform context-aware triage, con-strained payload proposal, evidence-grounded response analysis, false-alarm suppression, and actionable fix guidance. Empirical evaluation across WebGoat v2023.4, DVWA v1.9, bWAPP v2.2, and five authorized staging replicas of disclosed targets — spanning 1,247 endpoints and 462 labeled ground-truth flaws shows that the proposed approach raises micro-averaged F1 from 0.58 to 0.82 over scanner-only baselines, lowers the false-alarm rate from 23.4% to 7.2% (McNemar p<0.001), and shortens analyst review time by 43%, from 47 to 27 minutes per engagement (Wilcoxon p=0.008). Over 2,500 test executions, the safety layer blocked every destructive candidate payload with zero unsafe executions. A structured 10-person evaluation with five security practitioners and five application developers con-firmed statistically significant improvements in comprehension speed, report clarity, and remediation actionability (p<0.01).